Researchers have created an AI-powered blood test that can spot hidden signs of heart disease years before symptoms appear. This new tool, called CardiOmicScore, could predict major cardiovascular risks up to 15 years in advance.
The system was developed by a team from the University of Hong Kong (HKUMed). Their findings were published in Nature Communications.
Predicting Heart Disease Early
Cardiovascular diseases are the leading cause of death worldwide. In 2022 alone, they caused about 19.8 million deaths.
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Start Your News DetoxDoctors usually check for heart disease risk using factors like age, blood pressure, and smoking habits. However, these methods might miss early biological changes that happen before a disease can be diagnosed. This means many patients miss the chance to take preventive action.
Genetic risk scores have been gaining attention, but a person's genetic risk is set at birth. It doesn't change over time. This means these scores can't show how lifestyle changes might affect health right now. There's a clear need for tools that can show a person's current health status and give early warnings for heart diseases.
The HKUMed team used deep learning to combine different types of biological data, including genomics, metabolomics, and proteomics. They used a large amount of population data from the UK Biobank. They looked at 2,920 proteins and 168 metabolites found in blood samples. These molecular signals act like "real-time recorders" of the body, showing subtle changes in the immune system, metabolism, and blood vessel health.
Professor Zhang Qingpeng, an Associate Professor at HKUMed, explained that genes show a person's baseline health risk. But proteins and metabolites show their current physical health. He noted that their AI tool decodes these complex signals. This helps doctors and patients find risks much earlier, which could change the course of a disease through timely lifestyle changes and early prevention.
Accurate Predictions for Six Diseases
The CardiOmicScore turns complex molecular measurements into personal risk scores. It works much better than traditional genetic risk scores. When combined with clinical details like age and gender, the model greatly improved prediction accuracy for six common cardiovascular diseases. It could identify higher risk up to 15 years before symptoms appeared.
This research shows a shift in personalized medicine. It moves away from a fixed, gene-focused model to a more dynamic approach based on many types of biological data. In the future, a small blood sample might be enough to create a full heart disease risk profile for several conditions.
Professor Zhang hopes this technology will help identify and prevent diseases before they develop. He believes that by moving health management from treating diseases to predicting and preventing them, they can make a lasting impact on public health and individual patient care.
Deep Dive & References
AI-based multiomics profiling reveals complementary omics contributions to personalized prediction of cardiovascular disease - Nature Communications, 2026
